Transformer-Based Model Forecasts Airport Terminal Passenger Queues Up to Two Hours Ahead
Researchers have developed a machine learning framework using Transformer architecture to predict passenger queue lengths and waiting times at airport departure gates and security checkpoints. The model learns from historical operational data including queue lengths, waiting times, and passenger throughput at check-in areas. The approach could enable airports to manage congestion proactively and optimize staff allocation in real-time.
A new study accepted at the 2026 Digital Avionics Systems Conference presents a Transformer-based neural network designed to forecast passenger queues in airport terminals up to two hours in advance. The framework addresses the challenge of time-varying passenger demand and heterogeneous usage patterns across multiple departure facilities by learning from historical operational data. The model uses past queue lengths, waiting times at departure gates and security checkpoints, and passenger throughput at check-in islands as inputs, with separate prediction heads for each facility type. Experimental results demonstrate accurate forecasts across the two-hour prediction window. The researchers propose this approach as a practical tool for real-time decision support, enabling airport operators to implement proactive congestion management strategies and dynamically reallocate staff resources.
What's missing
The paper does not specify which airports or datasets were used for validation, the baseline models used for comparison, or quantitative performance metrics (e.g., mean absolute error, root mean squared error). Additionally, the study's limitations regarding scalability to different airport architectures, seasonal variations, or extraordinary events (e.g., weather disruptions, security incidents) are not discussed in the abstract.
What different sources said
- arXiv cs.LGCenter
Airport Terminal Passenger Queue Forecasting for Departure Gates and Security Checkpoints
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